Premedication with acetazolamide: Is its use for postoperative pain and stress control after laparoscopic ovariectomy in dogs ruled out?

BACKGROUND: Studies in human medicine have concluded that acetazolamide reduces pain associated with carbon dioxide insufflation during laparoscopic surgery. However, there are no published reports regarding the use of acetazolamide for this purpose in companion animals, despite the increasing popularity of laparoscopic techniques in veterinary medicine due to their advantages over open surgeries. OBJECTIVES: Thirty mixed-breed female dogs were included in the study and randomly assigned to one of three groups: OVE (median celiotomy ovariectomy; n = 10), OVEL (laparoscopic ovariectomy, n = 10) and OVELA (laparoscopic ovariectomy with acetazolamide preoperative administration; n = 10). Experienced surgeons performed all procedures, and the anaesthetic and analgesic protocols were identical for all animals. Acetazolamide was administered orally (at a dose of 25 mg/kg) 2 h prior to induction in the OVELA group. Postoperative pain was evaluated using serum cortisol, salivary cortisol, and the University of Melbourne Pain Scale (UMPS) Score. RESULTS: Any statistical differences were observed in the UMPS scores when the OVELA group was compared to the OVEL group at 1 h after surgery (p = 0.515), 12 h (p = 0.375) and 24 h (p = 0.242). Animals undergoing open surgery (OVE group) had significantly higher pain scores at all times after surgery when compared with OVEL and OVELA groups. A high positive correlation (r = 0.792; p = 0.01) was found between serum and saliva cortisol concentrations. Mean saliva cortisol concentration was not significantly lower for the OVELA group compared to the other groups. CONCLUSIONS: This study found evidence that preoperative administration of acetazolamide may be beneficial in managing postoperative pain in dogs after laparoscopic surgeries. However, further research with a larger sample size is needed to confirm this and to determine if acetazolamide should be included in a multimodal postoperative analgesia protocol for laparoscopic ovariectomy in dogs.

Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study.

In the last decades, several swarm-based optimization algorithms have emerged in the scientific literature, followed by a massive increase in terms of their fields of application. Most of the studies and comparisons are restricted to high-level languages (such as MATLAB®) and testing methods on classical benchmark mathematical functions. Specifically, the employment of swarm-based methods for solving energy-based acoustic localization problems is still in its inception and has not yet been extensively studied. As such, the present work marks the first comprehensive study of swarm-based optimization algorithms applied to the energy-based acoustic localization problem. To this end, a total of 10 different algorithms were subjected to an extensive set of simulations with the following aims: (1) to compare the algorithms' convergence performance and recognize novel, promising methods for solving the problem of interest; (2) to validate the importance (in convergence speed) of an intelligent swarm initialization for any swarm-based algorithm; (3) to analyze the methods' time efficiency when implemented in low-level languages and when executed on embedded processors. The obtained results disclose the high potential of some of the considered swarm-based optimization algorithms for the problem under study, showing that these methods can accurately locate acoustic sources with low latency and bandwidth requirements, making them highly attractive for edge computing paradigms.

Elephant Herding Optimization for Energy-Based Localization.

This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations, we approach it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics are applied to this type of problem. More specifically, an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as a performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.